Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms

By Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, Eduard H. Hovy. 2019

72 citations recorded by the source corpus.

Modeling what makes a request persuasive - eliciting the desired response from a reader - is critical to the study of propaganda, behavioral economics, and advertising. Yet current models can’t quantify the persuasiveness of requests or extract successful persuasive strategies. Building on theories of persuasion, we propose a neural network to quantify persuasiveness and identify the persuasive strategies in advocacy requests. Our semi-supervised hierarchical neural network model is supervised by the number of people persuaded to take actions and partially supervised at the sentence level with human-labeled rhetorical strategies. Our method outperforms several baselines, uncovers persuasive strategies - offering increased interpretability of persuasive speech - and has applications for other situations with document-level supervision but only partial sentence supervision.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Experience Grounds Language (2020, background): “In order to learn the effects language has on the world, an agent must participate in linguistic activity, such as negotiation (Yang et al., 2019a; He et al., 2018; Lewis et al., 2017), collaboration (Chai et al., 2017), visual disambiguation (Anderson et al., 2018; Lazaridou et al., 2017; Liu and…”
Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models (2020, background): “This approach resulted in a variety of visualizations and other attempts at model explanation being explored based, in part or in whole, on attention weights (Xu et al., 2015a; Mullenbach et al., 2018; Yang et al., 2019).”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “…in-6 stance, text that aims to convince others often uses various persuasion strategies (Yang et al., 2019a; Chen and Yang, 2021), argumentation techniques (Stab and Gurevych, 2014), rhetorical structures (Rapp, 2011), and the exchange of social support (Wang and Jurgens, 2018; Yang et al., 2019b).”
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (2019, background): “Yang et al. (2019) proposed a hierarchical neural network model to identify persuasion strategies in a semi-supervised fashion.”
Can Large Language Models Transform Computational Social Science? (2023, methodology): “This dataset was labeled by Yang et al. (2019a) with a fine-grained persuasive strategy taxonomy based on Cialdini (2003) that includes Evidence, Impact, Politeness, Reciprocity, Scarcity, and Emotion.”